# coding:utf-8
# Author : hiicy redldw
# Date : 2019/03/21

import tensorflow as tf
from tensorflow.python import graph_util
def freeze_graph(input_checkpoint, output_graph):
    '''
    :param input_checkpoint: xxx.ckpt(千万不要加后面的xxx.ckpt.data这种，到ckpt就行了!)
    :param output_graph: PB模型保存路径
    :return:
    '''
    # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
    # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径

    # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
    output_node_names = "inputs_placeholder,keep_prob,correct_sm" # 模型输入节点，根据情况自定义
    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
    graph = tf.get_default_graph() # 获得默认的图
    input_graph_def = graph.as_graph_def()  # 返回一个序列化的图代表当前的图

    with tf.Session() as sess:
        saver.restore(sess, input_checkpoint) # 恢复图并得到数据
        output_graph_def = graph_util.convert_variables_to_constants(  # 模型持久化，将变量值固定
            sess=sess,
            input_graph_def=input_graph_def,# 等于:sess.graph_def
            output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开

        with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
            f.write(output_graph_def.SerializeToString()) #序列化输出
        print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点
ck = "/aidfs/003/train-saver/digits/model.ckpt"
pb = '/aidfs/003/train-saver/digits/ocr.pb'
freeze_graph(ck,pb)

from tensorflow.python import pywrap_tensorflow
import os
# 示例
checkpoint_path=os.path.join("/aidfs/003/train-saver/digits","model.ckpt")
print(checkpoint_path)
reader=pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map=reader.get_variable_to_shape_map()
for key in var_to_shape_map:
    print('tensor_name: ', key)